A while ago I was wondering if I could use the Tableau Web Data Connector to upload files to a server running Python then process the data with a machine learning library (such as scikit-learn) and return the predicted values and model validation metrics in a Tableau extract. It worked quite well so I wrote a post on it which can be found here http://doubleee.pythonanywhere.com/wdcblog

I developed this connector some time ago, so it uses the WDC 1.1 version. It's a prototype so there are room for improvements...

TabPy seems really nice. Fully integrated executable Python code from a workbook. Beautiful! Although, the WDC does have a interface for building predictive models, which could come in handy for those analyst not skilled in Python or R.

Prayson, your best bet is to submit your questions as a new thread on the forums. If you're looking for 1-1 support, your best bet is to probably engage some consulting / training services offered by Tableau.

Thanks Tom. The data security issue is directed to Erik's Web Connector. When dealing with sensitive data, I want to know the how-s and where-s data is stored in pythonanywhere cloud and whether run this connection could be run in an internal sever.

I used my pythonanywhere account to host this WDC prototype as an example on how to connect and demonstrate the possibilities of a Web Data Connector using a ML library (scikit-learn) on the server side. It is not intended to handle sensitive data and I limited the file upload to a few MB.

However the Python code (using the web framework Flask) could preferably be placed and run from a local server or your local machine as long as it can handle http requests. I know when I was testing the WDC locally I connected Tableau to localhost:5000/wdc (5000 = the default port of Flask)

Yes it's true that not all business analysts and business users in need of predictive analytics are fluent in R or Python, but still they can have an excellent understanding of the business objective and the data thus providing valuable input variables for a model.

As demonstrated it works very well using a WDC with a ML library on the server providing predictive analytics in Tableau.

Although, I believe it would be even better if Tableau further developed the analytics capabilities and algorithms natively available in the desktop client. The k-means algorithm for clustering is a nice addition but additional algorithms in supervised learning would be much appreciated.